OpensSTF provides automated machine learning pipelines to deliver accurate, self-correcting and explainable forecasts of the load on the grid for the next 48 hours.
More information to follow...
Description
The energy transition poses new challenges to all parties in the energy sector. For grid operators, the rise in renewable energy and electrification of energy consumption leads to the capacity of the grid to near its physical constraints. Forecasting the load on the grid in the next hours to days is essential for anticipating on local congestion and making the most of existing assets.
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Screenshot of the operational dashboard showing the key functionality of OpenSTF
Repositories
| Link | Description |
|---|---|
| openstf | Automated Machine Learning Pipelines. Builds the Open Short Term Forecasting package |
| openstf-dbc | Provides (company specific) database connector for the OpenSTF package |
| openstf-offline-example | Provides Jupyter Notebooks showing how to use OpenSTF and apply it's functionality to your usecase |
| openstf-reference | Deploy the entire OpenSTF stack on your machine. Provides a reference implementation of the OpenSTF stack |